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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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NGS-Trex: an automatic analysis workflow for RNA-Seq data.

Ilenia Boria1, Lara Boatti, Igor Saggese

  • 1Department of Chemistry, University of Milano, Via Golgi 19, 20133, Milano, Italy.

Methods in Molecular Biology (Clifton, N.J.)
|January 12, 2015
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) RNA-Seq analysis is simplified by the NGS-Trex platform. This automated workflow provides comprehensive transcriptome profiling and differential gene expression analysis with minimal user interaction.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • RNA-Sequencing (RNA-Seq) enables rapid whole-transcriptome analysis using next-generation sequencing (NGS).
  • Decreasing NGS costs increase RNA-Seq popularity, but complex bioinformatics requirements limit accessibility.
  • Existing analysis pipelines demand significant bioinformatic expertise and infrastructure.

Purpose of the Study:

  • To present NGS-Trex, a fully automated workflow for comprehensive RNA-Seq data analysis.
  • To demonstrate a user-friendly web interface for transcriptome profiling and data mining.
  • To enable researchers to identify expressed genes, splice variants, and differentially expressed genes.

Main Methods:

  • Utilized the NGS-Trex platform for end-to-end RNA-Seq data analysis.
  • Employed a low-user-interaction, automated analysis workflow via a web interface.
  • Processed raw sequencing data to generate comprehensive transcriptome profiles.

Main Results:

  • NGS-Trex successfully profiles transcriptomes, identifying expressed genes and splice variants.
  • The platform detects known and novel splice variants.
  • Differential gene and transcript expression across experiments is accurately identified.

Conclusions:

  • NGS-Trex significantly lowers the barrier to entry for RNA-Seq data analysis.
  • The automated workflow democratizes transcriptome analysis for a wider research community.
  • NGS-Trex facilitates efficient discovery of biological insights from RNA-Seq data.